Summary of Synergistic Dual Spatial-aware Generation Of Image-to-text and Text-to-image, by Yu Zhao et al.
Synergistic Dual Spatial-aware Generation of Image-to-Text and Text-to-Image
by Yu Zhao, Hao Fei, Xiangtai Li, Libo Qin, Jiayi Ji, Hongyuan Zhu, Meishan Zhang, Min Zhang, Jianguo Wei
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to spatial image-to-text (SI2T) and spatial text-to-image (ST2I) tasks, which are fundamental to visual spatial understanding (VSU). Existing methods for standalone SI2T or ST2I perform poorly due to the difficulty of 3D-wise spatial feature modeling. The authors introduce a dual learning framework that models both tasks together, using a novel 3D scene graph (3DSG) representation to share and benefit from features between tasks. They also propose Spatial Dual Discrete Diffusion (SD^3), which uses intermediate features from 3D-to-image or 3D-to-text processes to guide the harder image-to-3D or text-to-3D processes, improving overall performance. Experimental results on the VSD dataset show significant outperformance of mainstream T2I and I2T methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help computers understand spatial things like rooms or buildings by converting images into words and vice versa. Right now, these tasks are done separately, but this doesn’t work well because it’s hard for computers to understand 3D spaces. The authors come up with a new approach that does both tasks together, using special features that can be shared between them. They also use an idea that makes the harder tasks easier by giving them hints from the easier ones. This helps the computer do better at understanding spatial things. |
Keywords
» Artificial intelligence » Diffusion
